Business Models for Data Journalism

Amidst all the interest and hope regarding data-driven journalism there is one question that newsrooms are always curious about: what are the business models?

While we must be careful about making predictions, a look at the recent history and current state of the media industry can help to give us some insight. Today there are many news organizations who have gained by adopting new approaches.

Terms like “data journalism”, and the newest buzzword “data science” may sound like they describe something new, but this is not strictly true. Instead these new labels are just ways of characterizing a shift that has been gaining strength over decades.

Many journalists seem to be unaware of the size of the revenue that is already generated through data collection, data analytics and visualization. This is the business of information refinement. With data tools and technologies it is increasingly possible to shed a light on highly complex issues, be this international finance, debt, demography, education and so on. The term “business intelligence” describes a variety of IT concepts aiming to provide a clear view on what is happening in commercial corporations. The big and profitable companies of our time, including McDonalds, Zara or H&M rely on constant data tracking to turn out a profit. And it works pretty well for them.

What is changing right now is that the tools developed for this space are now becoming available for other domains, including the media. And there are journalists who get it. Take Tableau, a company providing a suite of visualization tools. Or the “Big Data” movement, where technology companies use (often open source) software packages to dig through piles of data, extracting insights in milliseconds.

These technologies can now be applied to journalism. Teams at The Guardian and The New York Times are constantly pushing the boundaries in this emerging field. And what we are currently seeing is just the tip of the iceberg.

But how does this generate money for journalism? The big, worldwide market that is currently opening up is all about transformation of publicly available data into something our that we can process: making data visible and making it human. We want to be able to relate to the big numbers we hear every day in the news — what the millions and billions mean for each of us.

There are a number of very profitable data-driven media companies, who have simply applied this principle earlier than others. They enjoy healthy growth rates and sometimes impressive profits. One example: Bloomberg. The company operates about 300,000 terminals and delivers financial data to it’s users. If you are in the money business this is a power tool. Each terminal comes with a color coded keyboard and up to 30,000 options to look up, compare, analyze and help you to decide what to do next. This core business generates an estimated US $6.3 billion per year, at least this what a piece by the New York Times estimated in 2008. As a result, Bloomberg has been hiring journalists left, right and centre, they bought the venerable but loss-making “Business Week” and so on.

Another example is the Canadian media conglomerate today known as Thomson Reuters. They started with one newspaper, bought up a number of well known titles in the UK, and then decided two decades ago to leave the newspaper business. Instead they have grown based on information services, aiming to provide a deeper perspective for clients in a number of industries. If you worry about how to make money with specialized information, the advice would be to just read about the company’s history in Wikipedia.

And look at the Economist. The magazine has built an excellent, influential brand on its media side. At the same time the “Economist Intelligence Unit” is now more like a consultancy, reporting about relevant trends and forecasts for almost any country in the world. They are employing hundreds of journalists and claim to serve about 1.5 million customers worldwide.

And there are many niche data-driven services that could serve as inspiration: eMarketer in the US, providing comparisons, charts and advice for anybody interested in internet marketing. Stiftung Warentest in Germany, an institution looking into the quality of products and services. Statista, again from Germany, a start-up helping to visualize publicly available information.

Around the world there is currently a wave of startups in this sector, naturally covering a wide range of areas — for example, Timetric, which aims to “reinvent business research”, OpenCorporates, Kasabi, Infochimps and Data Market. Many of these are arguably experiments, but together they can be taken as an important sign of change.

Then there is the public media, which in terms of data-driven journalism is a sleeping giant. In Germany €7.2 billion per year are flowing into this sector. Journalism is a special product: if done well it is not just about making money, but serves an important role in society. Once it is clear that data journalism can provide better, more reliable insights more easily, some of this money could be used for new jobs in newsrooms.

With data journalism, it is not just about being first but about being a trusted source of information. In this multi-channel world, attention can be generated in abundance, but trust is an increasingly scarce resource. Data journalists can help to collate, synthesize and present diverse and often difficult sources of information in a way which gives their audience real insights into complex issues. Rather than just recycling press releases and retelling stories they’ve heard elsewhere, data journalists can give readers a clear, comprehensible and preferably customizable perspective with interactive graphics and direct access to primary sources. Not trivial, but certainly valuable.

So what is the best approach for aspiring data journalists to explore this field and convince management to support innovative projects?

The first step should be to look for immediate opportunities close to home: low hanging fruit. For example, you might already have collections of structured texts and data that you could use. A prime example of this is the “Homicide database” of the Los Angeles Times. Here data and visualizations are the core, not an afterthought. The editors collect all the crimes they find and only then write articles based on this. Over time, such collections are becoming better, deeper and more valuable.

This might not work the first time. But it will over time. One very hopeful indicator here is that the Texas Tribune and ProPublica, which are both arguably post-print media companies, reported that funding for their non-profit journalism organizations exceeded their goals much earlier than planned.

Becoming proficient in all things data — whether as a generalist or as a specialist focused on one aspect of the data food chain — provides a valuable perspective for people who believe in journalism. One well-known publisher in Germany recently said in an interview: “There is this new group who call themselves data journalists. And they are not willing to work for peanuts anymore”.